Limits...
Decoding the regulatory network of early blood development from single-cell gene expression measurements.

Moignard V, Woodhouse S, Haghverdi L, Lilly AJ, Tanaka Y, Wilkinson AC, Buettner F, Macaulay IC, Jawaid W, Diamanti E, Nishikawa S, Piterman N, Kouskoff V, Theis FJ, Fisher J, Göttgens B - Nat. Biotechnol. (2015)

Bottom Line: Here we describe a strategy to address this problem that combines gene expression profiling of large numbers of single cells with data analysis based on diffusion maps for dimensionality reduction and network synthesis from state transition graphs.Several model predictions concerning the roles of Sox and Hox factors are validated experimentally.Our results demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that underpin organogenesis.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK. [2] Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.

ABSTRACT
Reconstruction of the molecular pathways controlling organ development has been hampered by a lack of methods to resolve embryonic progenitor cells. Here we describe a strategy to address this problem that combines gene expression profiling of large numbers of single cells with data analysis based on diffusion maps for dimensionality reduction and network synthesis from state transition graphs. Applying the approach to hematopoietic development in the mouse embryo, we map the progression of mesoderm toward blood using single-cell gene expression analysis of 3,934 cells with blood-forming potential captured at four time points between E7.0 and E8.5. Transitions between individual cellular states are then used as input to develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model of blood development. Several model predictions concerning the roles of Sox and Hox factors are validated experimentally. Our results demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that underpin organogenesis.

Show MeSH

Related in: MedlinePlus

Regulatory network synthesis from single-cell expression profiles(a) Discretisation of 3,934 expression profiles for 33 TFs yields 3,070 unique binary states, 1448 of which can be connected by single-gene changes to yield a state graph. (b) Representation of resulting state graph, coloured by first embryonic stage appearing in each state. Blue, PS; green, NP; orange, HF; red, 4SG; purple, 4SFG−. Magnification of fate transition towards 4SG states, with for example Sox7 expression switching off along all routes. (c) Representation of synthesised asynchronous Boolean network models for core network of 20 TFs. Green edges indicate activation; red edges indicate repression. Square boxes represent AND operations. Circles connecting edges indicate multiple update rules.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4374163&req=5

Figure 3: Regulatory network synthesis from single-cell expression profiles(a) Discretisation of 3,934 expression profiles for 33 TFs yields 3,070 unique binary states, 1448 of which can be connected by single-gene changes to yield a state graph. (b) Representation of resulting state graph, coloured by first embryonic stage appearing in each state. Blue, PS; green, NP; orange, HF; red, 4SG; purple, 4SFG−. Magnification of fate transition towards 4SG states, with for example Sox7 expression switching off along all routes. (c) Representation of synthesised asynchronous Boolean network models for core network of 20 TFs. Green edges indicate activation; red edges indicate repression. Square boxes represent AND operations. Circles connecting edges indicate multiple update rules.

Mentions: We first discretized all 3,934 single-cell expression profiles to binary states and connected those states that differ in the expression of only one gene. The threshold for binary discretization was determined as described in Methods. This yielded a connected state-transition graph of 1,448 expression states, connected by single-gene transitions (Fig. 3a,b). The number of times each state occurs is indicated in Supplementary Fig. 8. The probability of seeing even one repeated state or neighbor in the whole theoretical state space is negligible, illustrating the non-random nature of the data. Most states that corresponded to the Runx1-GFP+ 4SG cells clustered together at one end of the state-transition graph, whereas states corresponding to cells from other time points were dispersed between two additional clusters. Likely developmental transitions were revealed, with specific genes consistently switching on or off along all routes linking the major clusters. We therefore considered this state-transition graph as a possible representation of developmental expression state changes based on single-gene switches, and next asked whether this could be used for regulatory network reconstruction. Notably, analysis of real and simulated populations of 20 cells showed that pools for the same stage clustered closely together, which masked variation and therefore would not have provided the number of transcriptional states required for network synthesis (Supplementary Fig. 9).


Decoding the regulatory network of early blood development from single-cell gene expression measurements.

Moignard V, Woodhouse S, Haghverdi L, Lilly AJ, Tanaka Y, Wilkinson AC, Buettner F, Macaulay IC, Jawaid W, Diamanti E, Nishikawa S, Piterman N, Kouskoff V, Theis FJ, Fisher J, Göttgens B - Nat. Biotechnol. (2015)

Regulatory network synthesis from single-cell expression profiles(a) Discretisation of 3,934 expression profiles for 33 TFs yields 3,070 unique binary states, 1448 of which can be connected by single-gene changes to yield a state graph. (b) Representation of resulting state graph, coloured by first embryonic stage appearing in each state. Blue, PS; green, NP; orange, HF; red, 4SG; purple, 4SFG−. Magnification of fate transition towards 4SG states, with for example Sox7 expression switching off along all routes. (c) Representation of synthesised asynchronous Boolean network models for core network of 20 TFs. Green edges indicate activation; red edges indicate repression. Square boxes represent AND operations. Circles connecting edges indicate multiple update rules.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4374163&req=5

Figure 3: Regulatory network synthesis from single-cell expression profiles(a) Discretisation of 3,934 expression profiles for 33 TFs yields 3,070 unique binary states, 1448 of which can be connected by single-gene changes to yield a state graph. (b) Representation of resulting state graph, coloured by first embryonic stage appearing in each state. Blue, PS; green, NP; orange, HF; red, 4SG; purple, 4SFG−. Magnification of fate transition towards 4SG states, with for example Sox7 expression switching off along all routes. (c) Representation of synthesised asynchronous Boolean network models for core network of 20 TFs. Green edges indicate activation; red edges indicate repression. Square boxes represent AND operations. Circles connecting edges indicate multiple update rules.
Mentions: We first discretized all 3,934 single-cell expression profiles to binary states and connected those states that differ in the expression of only one gene. The threshold for binary discretization was determined as described in Methods. This yielded a connected state-transition graph of 1,448 expression states, connected by single-gene transitions (Fig. 3a,b). The number of times each state occurs is indicated in Supplementary Fig. 8. The probability of seeing even one repeated state or neighbor in the whole theoretical state space is negligible, illustrating the non-random nature of the data. Most states that corresponded to the Runx1-GFP+ 4SG cells clustered together at one end of the state-transition graph, whereas states corresponding to cells from other time points were dispersed between two additional clusters. Likely developmental transitions were revealed, with specific genes consistently switching on or off along all routes linking the major clusters. We therefore considered this state-transition graph as a possible representation of developmental expression state changes based on single-gene switches, and next asked whether this could be used for regulatory network reconstruction. Notably, analysis of real and simulated populations of 20 cells showed that pools for the same stage clustered closely together, which masked variation and therefore would not have provided the number of transcriptional states required for network synthesis (Supplementary Fig. 9).

Bottom Line: Here we describe a strategy to address this problem that combines gene expression profiling of large numbers of single cells with data analysis based on diffusion maps for dimensionality reduction and network synthesis from state transition graphs.Several model predictions concerning the roles of Sox and Hox factors are validated experimentally.Our results demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that underpin organogenesis.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK. [2] Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.

ABSTRACT
Reconstruction of the molecular pathways controlling organ development has been hampered by a lack of methods to resolve embryonic progenitor cells. Here we describe a strategy to address this problem that combines gene expression profiling of large numbers of single cells with data analysis based on diffusion maps for dimensionality reduction and network synthesis from state transition graphs. Applying the approach to hematopoietic development in the mouse embryo, we map the progression of mesoderm toward blood using single-cell gene expression analysis of 3,934 cells with blood-forming potential captured at four time points between E7.0 and E8.5. Transitions between individual cellular states are then used as input to develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model of blood development. Several model predictions concerning the roles of Sox and Hox factors are validated experimentally. Our results demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that underpin organogenesis.

Show MeSH
Related in: MedlinePlus